Network Oral English Teaching System Based on Speech Recognition Technology and Deep Neural Network

With the development of computer technology, computer-aided instruction is being used more and more widely in the field of education. Based on speech recognition technology and deep neural network, this paper proposes an online oral English teaching system. Firstly, the speech recognition technology is introduced and its feature extraction is elaborated in detail. Then, three basic problems and three basic algorithms that need to be solved in speech recognition system using Markov model are discussed. The application of HMM technology in speech recognition system is studied, and some algorithms are optimized. The logarithmic processing of Viterbi algorithm, compared with the traditional algorithm, greatly reduces the amount of computation and solves the overflow problem in the operation process. By combining deep network with HMM, continuous speech signal modeling is realized. According to the characteristics of the DNN-HMM model, it is proposed that the model cannot model the long-term dependence of speech signals and train complex problems. Based on Kaldi, the model training comparison experiments of monophonon model, triphonon model and adding feature transformation technology are carried out to continuously improve the model performance. Finally, through simulation experiments, it is found that the recognition rate of the optimized DNN-HMM mixed model proposed in this paper is the highest, reaching 97.5%, followed by the HMM model, which is 95.4%, and the lowest recognition rate is the PNN model, which is 90.1%.